Added RLHF Blog

infocusp · Mar 16, 2024 · bf207c5 · bf207c5
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diff --git a/README.md b/README.md
@@ -7,7 +7,7 @@ A multi-part seminar series on Large Language Models (LLMs).
 
 ![Session 1](images/home_page/Large%20Language%20Models.png)
 
-<p align="center"><a href="https://xmind.works/share/cmFNh1uK?xid=SjTLV1U0">Large Language Models Full Topic List</a></p>
+<p align="center">Click here for <a href="https://xmind.works/share/cmFNh1uK?xid=SjTLV1U0">Large Language Models Full Topic List</a></p>
 
 ## ✨ [Emergence, Fundamentals and Landscape of LLMs](session_1)
 
@@ -34,14 +34,12 @@ Explore diverse applications of Large Language Models (LLMs) and the frameworks
 
 Coming soon...
 
-## ✨ Training and Evaluating LLMs On Custom Datasets
+## ✨ [Training and Evaluating LLMs On Custom Datasets](session_4)
 
 Delve into the intricacies of training and evaluating Large Language Models (LLMs) on your custom datasets. Gain insights into optimizing performance, fine-tuning, and assessing model effectiveness tailored to your specific data.
 
 ![Session 4](images/home_page/Session%204.png)
 
-Coming soon...
-
 ## ✨ Optimizing LLMs For Inference and Deployment Techniques
 
 Learn techniques to optimize Large Language Models (LLMs) for efficient inference. Explore strategies for seamless deployment, ensuring optimal performance in real-world applications.
@@ -50,7 +48,7 @@ Learn techniques to optimize Large Language Models (LLMs) for efficient inferenc
 
 Coming soon...
 
-## ✨ Open Challanges With LLMs
+## ✨ Open Challenges With LLMs
 
 Delve into the dichotomy of small vs large LLMs, navigating production challenges, addressing research hurdles, and understanding the perils associated with the utilization of pretrained LLMs. Explore the evolving landscape of challenges within the realm of Large Language Models.
 

diff --git a/...llms/Large Language Models-challanges.png → ...llms/Large Language Models-challenges.png b/...llms/Large Language Models-challanges.png → ...llms/Large Language Models-challenges.png
diff --git a/images/site/infocusp_logo_blue.png b/images/site/infocusp_logo_blue.png
diff --git a/images/site/infocusp_logo_blue.svg b/images/site/infocusp_logo_blue.svg
diff --git a/mkdocs.yaml b/mkdocs.yaml
@@ -5,7 +5,7 @@ docs_dir: .
 site_dir: ../site
 theme:
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-  # logo: assets/icons8-code-64.png
+  logo: images/site/infocusp_logo_blue.png
   palette:
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   features:
@@ -22,6 +22,7 @@ theme:
 markdown_extensions:
   - toc:
       permalink: true
+      toc_depth: 3
   - pymdownx.highlight:
       anchor_linenums: true
       line_spans: __span
@@ -41,6 +42,8 @@ extra_css:
 extra:
   generator: false
   social:
+    - icon: fontawesome/solid/globe
+      link: https://infocusp.com
     - icon: fontawesome/brands/linkedin
       link: https://in.linkedin.com/company/infocusp
     - icon: fontawesome/brands/github
@@ -51,4 +54,6 @@ extra:
       link: https://twitter.com/_infocusp
 plugins:
   - search
-  - same-dir
+  - same-dir
+  - mkdocs-jupyter:
+      ignore_h1_titles: True
diff --git a/requirements.txt b/requirements.txt
@@ -1,4 +1,5 @@
 mkdocs>=1.2.2
 mkdocs-material>=7.1.11
 mkdocs-static-i18n>=0.18
-mkdocs-same-dir>=0.1.3
+mkdocs-same-dir>=0.1.3
+mkdocs-jupyter>=0.16.1
diff --git a/session_1/README.md b/session_1/README.md
@@ -6,6 +6,7 @@
 Covers important building blocks of what we call an LLM today, where they came from, etc. and then we'll dive into the deep universe that has sprung to life around these LLMs.
 
 This session is aimed to help:
+
 * People who are new to LLMs
 * People who have  just started working on them
 * People who are working on different use cases surrounding LLMs and need a roadmap.

diff --git a/session_1/part_3_landscape_of_llms/README.md b/session_1/part_3_landscape_of_llms/README.md
@@ -465,9 +465,9 @@
 
 </details>
 
-## Challanges with LLMs
+## Challenges with LLMs
 
-![Challanges with LLMs](./../../images/session_1/part_3_landscape_of_llms/Large%20Language%20Models-challanges.png)
+![Challenges with LLMs](./../../images/session_1/part_3_landscape_of_llms/Large%20Language%20Models-challenges.png)
 
 <details markdown>
 

diff --git a/session_4/README.md b/session_4/README.md
@@ -0,0 +1,34 @@
+# Session 4 - Training and Evaluating LLMs On Custom Datasets
+
+<p align="center"><img src="../images/home_page/Session%204.png" alt="Session 4"/></p>
+
+This session aims to equip you with the knowledge to train Large Language Models (LLMs) by exploring techniques like unsupervised pretraining and supervised fine-tuning with various preference optimization methods. It will also cover efficient fine-tuning techniques, retrieval-based approaches, and language agent fine-tuning. Additionally, the session will discuss LLM training frameworks and delve into evaluation methods for LLMs, including evaluation-driven development and using LLMs for evaluation itself.
+
+This session is aimed to help:
+
+* People who are already familiar basics of LLMs and Transformers
+* People who already knows how to use pre-trained LLMs prompt engineering and RAG
+* People who want train or finetune their own LLMs on custom data.
+* People who want to lear how to evaluate LLMs
+
+## Outline
+
+### Part 1: Training Foundational LLMs
+
+Coming soon...
+
+### Part 2: [Finetuning LMs To Human Preferences](part_2_finetuning_lms_to_human_preferences/RLHF.ipynb)
+
+#### Details
+
+* Date: 14 March, 2024
+* Speaker: [Abhor Gupta](https://in.linkedin.com/in/abhor-gupta-565386145)
+* Location: [Infocusp Innovations LLP](https://www.infocusp.com/)
+
+#### Material
+
+* Recording: TODO
+
+### Part 3: LLM Training Frameworks
+
+Coming soon...